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Sreepathi PaiView profile

Associate Professor

• Compilers
• Heterogeneous Architectures
• GPU Algorithms
• +3 more

Sreepathi Pai is an Associate Professor of Computer Science at the Hajim School of Engineering & Applied Sciences, University of Rochester. He holds a PhD from the Indian Institute of Science. His research focuses on experimental computer systems, particularly the performance of computer programs, with emphasis on heterogeneous architectures combining CPUs and GPUs. He develops optimizing compilers for irregular/graph algorithms on GPUs and creates performance models for these systems. Key research interests include compilers, heterogeneous architectures, GPU algorithms, and performance modeling. His work addresses challenges in large-scale automata processing, graph processing frameworks like Groute, and modular GPU emulation (Horus). He collaborates on projects such as TriForce for multiphysics simulations and explores efficient electromagnetic algorithms for plasma simulations. Recent projects include formal methods for scientific computing (DANUBE), lightweight GPU performance simulation, and autotuning for volunteer computing. His research also spans statistical caching, compiler-assisted memory management, and parallel triangle counting algorithms. His work bridges theory and practice, with contributions to frameworks like PLASMA for heterogeneous accelerators and tools for analyzing memory utilization in shared-memory systems. He has published extensively on topics ranging from tensor core modeling to preemptive thread block scheduling in GPUs.

Souyoung JinView profile

Assistant Professor

• Computer Vision
• Machine Learning
• Cognitive Science
• +3 more

Souyoung Jin is an Assistant Professor in the Department of Computer Science at Dartmouth College. Her research focuses on Computer Vision, Machine Learning, and Cognitive Science with expertise in video understanding. She earned her B.S. from Dongguk University, M.S. from KAIST, Ph.D. from UMass Amherst, and completed a postdoc at MIT's CSAIL under Aude Oliva. She teaches courses like Machine Learning and Deep Learning, and leads research on AI systems mimicking human perception. Her lab explores multimodal tasks and synthetic data applications. She is an Area Chair for WACV 2025 and actively recruits motivated students for her lab. Education: B.S., Dongguk University M.S., Korea Advanced Institute of Science and Technology Ph.D., University of Massachusetts Amherst Postdoc, MIT Research Interests: Dr. Jin develops AI systems to understand human experiences through video analysis. Her work spans video understanding, synthetic data applications, and cognitive-inspired models. She emphasizes temporal context in low-resource regimes and cross-modal learning. Recent projects include BOLD Moments (fMRI-video datasets) and LangNav (language-based navigation). Grants & Labs: Her research is supported by grants focusing on synthetic video representations and multimodal fusion. She leads Dartmouth's Video Understanding Lab, fostering interdisciplinary collaborations between computer vision and cognitive science.

Claude DuhrView profile

Professor

• Theoretical Physics
• Quantum Field Theory
• Particle Physics
• +4 more

Claude Duhr is a Professor of Theoretical Physics at Bonn University, specializing in advanced mathematical and computational methods in particle physics. His research focuses on Feynman integrals, elliptic functions, Calabi-Yau geometries, and high-energy scattering amplitudes. He contributes to precision calculations in quantum field theories, including the Standard Model and its extensions. His work bridges theoretical physics and pure mathematics, addressing challenges in multi-loop calculations, modular forms, and algebraic geometry applications. Recent studies include antipodal self-duality in fishnet graphs, canonical differential equations for geometric models, and renormalization in effective field theories. Key research themes include scattering amplitudes, collider phenomenology, and integrability in gauge theories. His publications explore cutting-edge tools for Feynman integral evaluation and their implications for precision physics at colliders. Research Interests: Mathematical Physics, Quantum Field Theory, High-Energy Phenomenology No scientific awards or grants are listed in the provided texts. His work is supported by collaborative projects in theoretical physics and algebraic geometry.

Leonid LevinView profile

Professor

• Theory of Computation
• Randomness in Computing
• Algorithmic Complexity
• +6 more

Leonid Levin is a Professor in the College of Arts & Sciences, Department of Computer Science at Boston University since 1980, promoted to Full Professor in 1984. He holds dual PhDs from Moscow University (1972) and MIT (1979). His research focuses on computational complexity, randomness, algorithmic information theory, and foundational aspects of mathematics and computer science. Key roles include membership in the US National Academy of Sciences (2019) and the American Academy of Arts and Sciences (2014). He has held visiting professorships at institutions such as the Institut des Hautes Études Scientifiques (France), Heidelberg University (Germany), and Hebrew University (Jerusalem). Levin’s work bridges theoretical computer science and mathematics, with contributions to one-way functions, pseudorandomness, and Kolmogorov complexity. Notable publications include foundational papers on computational complexity and cryptography, such as A pseudorandom generator from any one-way function (1999) and The tale of one-way functions (2003). His research interests span algorithmic randomness, fault-tolerant systems, and interdisciplinary topics like taxation and valuation. Awards include the Guggenheim Fellowship (1993-94) and recognition as a Clay Mathematics Institute Scholar (2001-2002).

Tiago JanuarioView profile

Lecturer

• Graph Theory
• Combinatorial Optimization
• Algorithms Design
• +4 more

Tiago Januario is a Lecturer in the Department of Computer Science at Boston University, part of the College of Arts & Sciences. Previously, he held a Tenured Assistant Professor position at Universidade Federal da Bahia (UFBA), Brazil. He earned his PhD from Universidade Federal de Minas Gerais (UFMG) in 2015, following an MS (2011) and undergraduate degree (2009) in Computer Science from Universidade Federal de Viçosa (UFV) and UFMG respectively. His research focuses on graph theory, combinatorial optimization, and algorithm design, with notable contributions to scheduling problems, telecommunications optimization, and AI applications. His work spans theoretical computer science, discrete mathematics, and practical software engineering challenges. He teaches courses like Design and Analysis of Algorithms and Formal Languages and Automata Theory. Recent publications emphasize scalable heuristic solutions for resource allocation in cloud-fog networks, trade-off analysis in software systems, and innovative scheduling algorithms using graph theory principles. His work bridges theoretical foundations with real-world applications in networking and adaptive software systems. Januario's advising and grant activities are not explicitly detailed, but his extensive teaching and research history suggest strong engagement in academic mentorship. His research lab likely explores interdisciplinary topics at the intersection of optimization, algorithms, and network systems.

• Theoretical Computer Science
• Algorithms
• Robotics
• +3 more

Dr Duncan Adamson is a Lecturer at the School of Computer Science, University of St Andrews. His research focuses on theoretical computer science, algorithms, robotics, and discrete mathematics. He has contributed to areas such as robot scheduling, temporal graphs, and combinatorial optimization. Recent work includes collision-free path planning for robots and harmonious colourings of temporal networks. He received the Best Paper Award at SAND 2024 for his contributions to algorithmic foundations. His publications span conferences like LATIN, ALGOWIN, and RP, addressing topics from graph theory to distributed systems. His research combines algorithm design with applications in robotics and computational chemistry. Education details are not explicitly stated in the provided texts. His research interests emphasize algorithmic solutions for complex systems, with a focus on graph structures and combinatorial problems. Collaborations include work on crystal structure prediction with chemists and computer scientists, demonstrating interdisciplinary impact. Awards: Best Paper Award SAND 2024 Advising/Grants: No specific grants or advisees listed in the texts provided. Labs/Teams: Affiliated with the School of Computer Science research groups, though specific lab names are not mentioned.

Lei FangView profile

Lecturer

• Bayesian Machine Learning
• Statistical Learning
• Wireless Sensor Networks
• +4 more

Dr. Lei Fang is a Lecturer in the School of Computer Science at the University of St Andrews. Their research focuses on Bayesian machine learning, statistical learning, wireless sensor networks, and adversarial machine learning. They advise PhD students Zhongliang Guo and Weiye Li. Key research interests include Bayesian nonparametrics, transfer learning, and human activity recognition through sensor data analysis. Dr. Fang's work spans interdisciplinary areas such as federated learning for healthcare applications and security in neural networks. They are affiliated with the Centre for Research into Ecological & Environmental Modelling, contributing to projects involving complex system analysis and sensor network optimization. Recent publications highlight advancements in adversarial attacks on signature verification systems, link prediction in heterogeneous social networks, and AI-driven software repository analysis. Their research emphasizes practical applications in cybersecurity, healthcare monitoring, and environmental modeling.

Subhash KhotView profile

Professor

• Algorithms
• Computational Complexity
• Computational Intractability
• +6 more

Subhash Khot is a Silver Professor of Computer Science at the Courant Institute of Mathematical Sciences, New York University (NYU). His research focuses on algorithms, computational complexity, and computational intractability, particularly in the areas of hardness of approximation, probabilistically checkable proofs (PCPs), and metric embeddings. He completed his Ph.D. at Princeton University under Sanjeev Arora in 2003, followed by postdoctoral work at the Institute for Advanced Study (IAS) and roles at Georgia Tech and the University of Chicago before joining NYU in 2007. His research has produced groundbreaking results, including foundational contributions to the Unique Games Conjecture and its implications for approximation algorithms. He has authored numerous influential papers in top conferences like FOCS, STOC, and SODA, addressing topics ranging from PCPs to learning theory and algorithmic lower bounds. His work has been recognized with awards such as the Best Paper Award at FOCS 2004 and 2005, and the Best Student Paper Award at Complexity 2003. Khot has taught courses on algorithms, computational complexity, and theory of computation at NYU, Georgia Tech, and the University of Chicago. His teaching emphasizes algorithm design, NP-completeness, and advanced topics like geometric methods in algorithm design. He has advised multiple students through his research and courses, contributing to the education of future computer scientists. His research also explores applications of computational complexity to areas like metric embeddings, learning theory, and concrete lower bounds in communication complexity. Khot’s work bridges theoretical computer science with practical algorithm design, making him a leading figure in computational theory.

• Extremal Combinatorics
• Probabilistic Combinatorics
• Asymptotic Enumeration
• +3 more

Jinyoung Park is an Assistant Professor at the Courant Institute of Mathematical Sciences, New York University. His research focuses on extremal and probabilistic combinatorics, asymptotic enumeration, and threshold phenomena in random discrete structures. He completed his Ph.D. at Rutgers University (2020) and held postdoctoral positions at Stanford University and the Institute for Advanced Study. His work is supported by an NSF grant and a Sloan Fellowship. Notable awards include the AMS Conant Prize (2025) and the Maryam Mirzakhani New Frontiers Prize (2023). Education: Ph.D., Mathematics, Rutgers University (2020) Postdoctoral Experience: Visiting Assistant Professor at Courant (2023), Szegö Assistant Professor at Stanford (2021-2022), Postdoctoral Member at IAS (2020-2021) Research Interests: Extremal Combinatorics (e.g., Dedekind’s problem, antichains), Probabilistic Combinatorics (threshold phenomena, random graphs), and Asymptotic Enumeration (Hamming cube colorings, independent sets). His work bridges combinatorial structures and probabilistic methods. Recent Contributions: Key papers include resolving the Kahn-Kalai conjecture (2024), threshold phenomena for random structures (2023), and enumerative results on Hamming cube colorings (2025). These reflect his expertise in combinatorial optimization and probabilistic thresholds. Awards: AMS Conant Prize, Dénes König Prize, Sloan Fellowship Teaching: Courses at NYU (Probability, Combinatorics), Stanford (Number Theory, Linear Algebra), and Rutgers (Calculus, Linear Algebra) Labs/Teams: Co-organizes the NYU Theory Seminar. Active in conferences globally, including plenary talks at the 2026 International Congress of Mathematicians.

• Graph Theory
• Discrete Mathematics
• Combinatorics
• +3 more

Stefan Felsner is a Professor at the Institut für Mathematik of Technische Universität Berlin , specializing in Algorithmic and Discrete Mathematics . His work focuses on Graph Theory, Combinatorics, Discrete Geometry, and Order Theory. He leads the Discrete Mathematics Group and has been involved in DFG-funded projects on geometric representations of graphs, arrangements, and order geometry. Education & Career: Studied Mathematics at the University of Vienna. Worked as a research assistant at TU Berlin (1988–1993), then at Freie Universität Berlin (1993–2003) before becoming a Professor at TU Berlin in 2003. He earned his Habilitation in 1997 and has held visiting positions in the U.S. and Canada. Research Interests: Graph drawing, geometric graphs, arrangements of lines/pseudolines, order dimension, and combinatorial structures. He authored the book Geometric Graphs and Arrangements and contributes to editorial boards of ORDER and DMTCS . Recent Work: Focuses on contact representations of planar graphs, pseudocircle arrangements, and flip graph connectivity. His recent articles address convex drawings, Hamiltonian cycles in convex graphs, and counting pseudoline arrangements. He actively participates in the Mittagsseminar for discrete mathematics discussions.

Frank ChanView profile

Associate Professor

• Evolutionary Biology
• Genetics & Heredity
• Genomics
• +5 more

Frank Chan is an Associate Professor in the Faculty of Science and Engineering at the University of Groningen, leading the Chan lab - Quantitative Genetics and Genomics. He holds a PhD in Developmental Biology from Stanford University School of Medicine (2009) and a BA in Molecular Biology & Biochemistry from Wesleyan University (2003). His research focuses on evolutionary genomics, particularly in sticklebacks, mice, and butterfly species, exploring topics like genomic basis of adaptation, haplotype analysis, and hybridization. Key research interests include evolutionary genetics, quantitative biology, and the application of genomic tools to study parallel evolution and hybridization. He has received prestigious grants such as the ERC Starting Grant (2014) and ERC Proof of Concept (2022), and fellowships including the Alexander von Humboldt and EMBO awards. Recent work includes studies on haplotype tagging in butterflies (PNAS 2021), genomic analysis of mouse limb evolution (eLife 2019), and structural variation in stickleback pelvic reduction (Science 2010). His lab also investigates T cell receptor diversity in mice and population genomics of Faroe Island mice. Awards: ERC Grants, Humboldt Fellowship, VolkswagenStiftung Grant Lab: Chan Lab - Quantitative Genetics & Genomics Positions: Board of Reviewing Editor at eLife (2023–2024), Former Max Planck Research Group Leader (2012–2023)

Liam SolusView profile

Assistant Professor

• Combinatorics
• Algebraic and Geometric Combinatorics
• Discrete Geometry
• +5 more

Liam Solus is an Assistant Professor of Mathematics at the Department of Mathematics, KTH Royal Institute of Technology in Stockholm, Sweden. He is affiliated with the ALGEBRA COMBINATORICS & TOPOLOGY division and the Digital Futures Faculty, focusing on research in combinatorics, discrete mathematics, algebraic statistics, and their applications in artificial intelligence and causal inference. His work is supported by grants from WASP (Wallenberg AI, Autonomous Systems, and Software Program), the Swedish Research Council, and the Göran Gustafsson Foundation. Education: PhD in Mathematics (University of Kentucky, 2015), postdoctoral fellowships at MIT, IST Austria, and the Max Planck Institute. Current research projects include causal AI for mobility behavior analysis (cAIMBER), combinatorial and geometric approaches to statistical models, and algebraic methods in machine learning. Research Interests: Algebraic and geometric combinatorics, lattice polytopes, causal inference, graphical models, algebraic statistics. Key contributions include studies on Ehrhart theory, Bayesian networks, and combinatorial sequences like Eulerian polynomials and Fibonacci numbers in causal structures. Awards: Large Prize for Young Researchers (Göran Gustafsson Foundation, 2023). Supervision includes PhD students Danai Deligeorgaki and former student Petter Restadh, along with postdocs in algebraic statistics and combinatorics. Labs/Teams: Co-PI of cAIMBER, member of Digital Futures, organizer of international workshops (e.g., AlCoVE 2024 in VR). Teaching includes courses on statistical learning, algebraic statistics, and probabilistic graphical models.

Nysret MusliuView profile

Associate Professor

• Artificial Intelligence
• Combinatorial Optimization
• Heuristic Optimization
• +5 more

Associate Professor Dr. Nysret Musliu is a faculty member at TU Wien's Faculty of Informatics, leading the Christian Doppler Laboratory for Artificial Intelligence and Optimization for Planning and Scheduling. He holds a PhD (2001) and habilitation (2007) from TU Wien. His research focuses on AI techniques, combinatorial optimization, scheduling, and heuristic methods applied to real-world problems like workforce scheduling and timetabling. Education: PhD in Computer Science (2001) TU Wien Habilitation in Applied Computer Science (2007) TU Wien Master's degree (1996) University of Prishtina Research Interests: Artificial Intelligence Techniques Combinatorial Optimization Heuristic Algorithms Machine Learning Integration Hybrid Techniques for Scheduling Problems Grants: Christian Doppler Laboratory (2017-2024), FWF Projects, Softnet Austria, and industry collaborations. Advises PhD students in optimization and scheduling domains. Labs/Teams: Christian Doppler Laboratory, Databases and Artificial Intelligence Group.

Shanxin YuanView profile

Lecturer

• 3D Computer Vision
• Reconstruction and Rendering
• Multimodal Learning
• +5 more

Dr. Shanxin Yuan is a Lecturer in Digital Environment at the School of Electronic Engineering and Computer Science, Queen Mary University of London. Previously, he worked as a Senior Research Scientist at Huawei Noah’s Ark Lab, London. His research focuses on computer vision and machine learning, particularly 3D digital humans, computational photography, and multimodal learning. Key research interests include hand pose estimation, neural rendering, and image restoration techniques such as super-resolution and HDR imaging. Research Interests: 3D Computer Vision, Reconstruction, and Rendering Neural Rendering for Deformable Objects Low-level Vision and Image Restoration Multimodal Learning and Vision-Language Integration Applications in Immersive Gaming, Music Understanding, and Fashion AI Recent Research Trends: Recent publications emphasize advancements in 3D hand modeling, neural radiance fields, and adaptive image restoration techniques. Work on motion retargeting and spatial-temporal modeling reflects growing focus on dynamic human digitization and realistic rendering. Labs & Affiliations: Active in the Digital Environment Research Institute (DERI) and the Centre for Multimodal AI , collaborating on projects that bridge computer vision with real-world applications.

• Artificial Intelligence
• Machine Learning
• Data Science
• +4 more

Dr. Fabrizio Smeraldi is a Senior Lecturer in Artificial Intelligence at Queen Mary University of London's School of Electronic Engineering and Computer Science. His research spans machine learning applications in health informatics, energy systems, and bioinformatics. He teaches postgraduate Computer Programming and develops software tools like LiBEIS and GEOexplorer. His work integrates computational methods with real-world challenges, including healthcare data analysis, battery management, and cybersecurity. Research Interests Dr. Smeraldi focuses on machine learning for health informatics (e.g., mining patient conversations), energy storage systems (battery SOC estimation), and bioinformatics (gene expression analysis). He also explores cybersecurity investments and computer vision techniques for feature extraction. Key Trends in Publications Recent work emphasizes interdisciplinary applications: combining ML with healthcare data, developing energy storage tools, and advancing bioinformatics platforms. His methods bridge computational theory with practical domains like public health and genomics. Grants & Advising No specific grants or advisees listed, though his research indicates collaborative projects in health tech and energy systems. His lab develops open-source tools for scientific communities.